Purpose: Immune checkpoint inhibitors (ICIs) have revolutionised the treatment of solid tumours with dramatic and durable responses seen across multiple tumour types. However, identifying patients who will respond to these drugs remains challenging, particularly in the context of advanced and previously treated cancers.
Experimental design: We characterised fresh tumour biopsies from a heterogeneous pan-cancer cohort of 98 patients with metastatic predominantly pre-treated disease through the Personalized OncoGenomics (POG) program at BC Cancer using whole genome and transcriptome analysis (WGTA). Baseline characteristics and follow up data were collected retrospectively.
Results: We found that tumour mutation burden (TMB), independent of mismatch repair status, was the most predictive marker of time to progression (TTP, p=0.007), but immune related CD8+ T cell and M1-M2 macrophage ratio scores were more predictive for overall survival (OS) (p=0.0014 and 0.0012 respectively). While CD274 (PD-L1) gene expression is comparable to protein levels detected by immunohistochemistry (IHC), we did not observe a clinical benefit for patients with this marker. We demonstrate that a combination of markers based on WGTA provides the best stratification of patients (p=0.00071, OS), and also present a case study of possible acquired resistance to pembrolizumab in a non-small cell lung cancer (NSCLC) patient.
Conclusions: Interpreting the tumour-immune interface to predict ICI efficacy remains challenging. WGTA allows for identification of multiple biomarkers simultaneously that in combination may help to identify responders, particularly in the context of a heterogeneous population of advanced and previously treated cancers, thus precluding tumour type-specific testing.
Introduction: Carcinogenesis is driven by an array of complex genomic patterns; these patterns can render an individual resistant or sensitive to certain chemotherapy agents. The Personalized Oncogenomics (POG) project at BC Cancer has performed integrative genomic analysis of whole tumour genomes and transcriptomes for over 700 patients with advanced cancers, with an aim to predict therapeutic sensitivities. The aim of this study was to utilize the POG genomic data to evaluate a discrete set of biomarkers associated with chemo-sensitivity or-resistance in advanced stage breast and colorectal cancer POG patients.
Methods: This was a retrospective multi-centre analysis across all BC CANCER sites. All breast and colorectal cancer patients enrolled in the POG program between July 1, 2012 and November 30, 2016 were eligible for inclusion. Within the breast cancer population, those treated with capecitabine, paclitaxel, and everolimus were analyzed, and for the colorectal cancer patients, those treated with capecitabine, bevacizumab, irinotecan, and oxaliplatin were analyzed. The expression levels of the selected biomarkers of interest (EPHB4, FIGF, CD133, DICER1, DPYD, TYMP, TYMS, TAP1, TOP1, CKDN1A, ERCC1, GSTP1, BRCA1, PTEN, ABCB1, TLE3, and TXNDC17) were reported as mRNA percentiles.
Results: For the breast cancer population, there were 32 patients in the capecitabine cohort, 15 in the everolimus cohort, and 12 in the paclitaxel cohort. For the colorectal cancer population, there were 29 patients in the bevacizumab cohort, 12 in the oxaliplatin cohort, 29 in the irinotecan cohort, and 6 in the capecitabine cohort. Of the biomarkers evaluated, the strongest associations were found between Bevacizumab-based therapy and DICER1 (P = 0.0445); and between capecitabine therapy and TYMP (P = 0.0553).
Conclusions: Among breast cancer patients, higher TYMP expression was associated with sensitivity to capecitabine. Among colorectal cancer patients, higher DICER1 expression was associated with sensitivity to bevacizumab-based therapy. This study supports further assessment of the potential predictive value of mRNA expression of these genomic biomarkers.
Despite a deeper molecular understanding, human glioblastoma remains one of the most treatment refractory and fatal cancers. It is known that the presence of macrophages and microglia impact glioblastoma tumorigenesis and prevent durable response. Herein we identify the dual function cytokine IL-33 as an orchestrator of the glioblastoma microenvironment that contributes to tumorigenesis. We find that IL-33 expression in a large subset of human glioma specimens and murine models correlates with increased tumor-associated macrophages/monocytes/microglia. In addition, nuclear and secreted functions of IL-33 regulate chemokines that collectively recruit and activate circulating and resident innate immune cells creating a pro-tumorigenic environment. Conversely, loss of nuclear IL-33 cripples recruitment, dramatically suppresses glioma growth, and increases survival. Our data supports the paradigm that recruitment and activation of immune cells, when instructed appropriately, offer a therapeutic strategy that switches the focus from the cancer cell alone to one that includes the normal host environment.
The discovery that the immunomodulatory imide drugs (IMiDs) possess antitumor properties revolutionized the treatment of specific types of hematological cancers. Since then, much progress has been made in understanding why the IMiDs are so efficient in targeting the malignant clones in difficult to treat diseases. Despite their efficacy, IMiD resistance arises eventually. Herein we summarize the mechanisms of sensitivity and resistance to lenalidomide in del(5q) myelodysplastic syndrome and multiple myeloma, two diseases in which these drugs are at the therapeutic frontline. Understanding the molecular and cellular mechanisms underlying IMiD efficacy and resistance may allow development of specific strategies to eliminate the malignant clone in otherwise incurable diseases.
The androgen receptor (AR) is a validated therapeutic target for prostate cancer and has been a focus for drug development for more than six decades. Currently approved therapies that inhibit AR signaling, such as enzalutamide, rely solely on targeting the AR ligand-binding domain and, therefore, have limited efficacy on prostate cancer cells that express truncated, constitutively active AR splice variants (AR-Vs). The LNCaP95 cell line is a human prostate cancer cell line that expresses both functional full-length AR and AR-V7. LNCaP95 is a heterogeneous cell population that is resistant to enzalutamide, with its proliferation dependent on transcriptionally active AR-V7. The purpose of this study was to identify a LNCaP95 clone that would be useful for evaluating therapies for their effectiveness against enzalutamide-resistant prostate cancer cells. Seven clones from the LNCaP95 cell line were isolated and characterized using morphology, in vitro growth rate, and response to ralaniten (AR N-terminal domain inhibitor) and enzalutamide (antiandrogen). In vivo growth of the clones as subcutaneous xenografts was evaluated in castrated immunodeficient mice. All of the clones maintained the expression of full-length AR and AR-V7. Cell proliferation of the clones was insensitive to androgen and enzalutamide but importantly was inhibited by ralaniten, which is consistent with AR-Vs driving the proliferation of parental LNCaP95 cells. In castrated immunodeficient animals, the growth of subcutaneous xenografts of the D3 clone was the most reproducible compared to the parental cell line and other clones. These data support that the enzalutamide-resistant LNCaP95-D3 subline may be suitable as a xenograft tumor model for preclinical drug development with improved reproducibility.
EGFR T790M testing is the standard of care for activating EGFR mutation (EGFRm) non-small cell lung cancer (NSCLC) progressing on 1st/2nd generation TKIs to select patients for osimertinib. Despite sensitive assays, detection of circulating tumour deoxyribonucleic acid (ctDNA) is variable and influenced by clinical factors. The number and location of sites of progressive disease at time of testing were reviewed to explore the effect on EGFR ctDNA detection. The prognostic value of EGFR ctDNA detection on survival outcomes was assessed.
The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.
With the rising incidence of early-onset pancreatic cancer (EOPC), molecular characteristics that distinguish early onset pancreatic ductal adenocarcinoma (PDAC) tumors from those arising at a later age are not well understood.
We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets, we show that Epiclomal outperforms non-probabilistic methods and can handle the inherent missing data characteristic that dominates single-cell CpG genome sequences. Using newly generated single-cell 5mCpG sequencing data, we show that Epiclomal discovers sub-clonal methylation patterns in aneuploid tumour genomes, thus defining epiclones that can match or transcend copy number-determined clonal lineages and opening up an important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.